Microbial marker-based system for predicting prognosis of laryngeal squamous cell carcinoma

Abstract

The invention discloses a system for predicting / assisting in predicting prognosis of laryngeal squamous cell carcinoma based on a microbial marker, which associates recurrence of laryngeal squamous cell carcinoma with a microbial community for the first time, integrates a cancer-promoting microorganism fusobacterium and a cancer-inhibiting microorganism Serratia, and can be used for predicting the prognosis of laryngeal squamous cell carcinoma, so that the recurrence of laryngeal squamous cell carcinoma and the recurrence of laryngeal squamous cell carcinoma can be predicted and the prognosis of laryngeal squamous cell carcinoma can be predicted. According to the method, the content of microorganisms is detected on the basis of quantitative PCR, risk score assignment is performed according to-delta Ct obtained by a cycle threshold Ct of the microorganisms, and then the risk score is calculated according to a prognosis risk scoring formula, so that the prognosis risk of the laryngeal squamous cell carcinoma, especially the recurrence risk, can be classified according to a calculation result; the method can provide a new direction for prognosis prediction of laryngeal squamous cell carcinoma, especially for prediction of recurrence risk and disease-free survival rate within 5 years.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and more particularly to a system based on microbial biomarkers for predicting the prognosis of laryngeal squamous cell carcinoma. Background Technology

[0002] Laryngeal squamous cell carcinoma (LSCC) is a malignant tumor that occurs in the throat, commonly known as "throat cancer." The most obvious early sign of this cancer is sudden hoarseness, especially when the tumor is located on the vocal cords. If the tumor is located in the epiglottis or below the vocal cords, early symptoms may include mild throat discomfort, a persistent cough, or a feeling of something stuck in the throat. As the tumor grows, symptoms may include difficulty breathing, severe coughing, pain when swallowing, and swelling and pain in the neck. In severe cases, cancer cells may spread to the cervical lymph nodes. The main treatment is surgical removal of the tumor, and radiation therapy may be chosen in some cases. Prognostic assessment is crucial for the selection of treatment plans and efficacy. Currently, the classification or assessment of laryngeal cancer prognosis mainly relies on the AJCC TNM staging system based on anatomical features and clinical characteristics of lymphovascular invasion, without considering the biological heterogeneity of the tumor microenvironment (such as differences in microbial composition and function). This leads to insufficient accuracy in predicting recurrence in early-stage laryngeal cancer patients, potentially resulting in undertreatment or overtreatment. Furthermore, current research on cancer-related microorganisms often focuses on single oncogenic microorganisms (such as...). Fusobacterium ), and has not yet taken into account cancer-suppressing microorganisms (such as Serratia The synergistic regulatory effect of microbial biomarkers and the technical solutions for integrating microbial biomarkers for systematic clinical application to predict cancer prognosis. Summary of the Invention

[0003] Therefore, based on the above background, this invention provides a system for predicting / assisting the prognosis of laryngeal squamous cell carcinoma by organically integrating tumor-promoting and tumor-suppressing microorganisms, thereby providing a new direction for predicting the prognosis of laryngeal squamous cell carcinoma, especially the risk of recurrence.

[0004] One of the technical solutions of the present invention: Application of microbial biomarkers in the preparation of products or systems for predicting the prognosis of laryngeal squamous cell carcinoma, said microbial biomarkers being derived from Serratia spp. Serratia and Fusobacterium Fusobacterium composition.

[0005] The second technical solution of the present invention: A kit designed to detect the levels of the aforementioned microbial markers.

[0006] Furthermore, it includes primers for detecting the aforementioned microbial markers.

[0007] Furthermore, the primers include a first primer pair, a second primer pair, and a universal primer; The first primer pair includes a first forward primer and a first reverse primer, the nucleotide sequence of which is as follows: SEQ ID NO.1 As shown: GTTTCCCAGACATTACTCAC, the nucleotide sequence of the first reverse primer is as follows: SEQ ID NO.2 As shown: AGCTTTTAATTGAAGAGTTTG; The second primer pair includes a second forward primer and a second reverse primer, the nucleotide sequence of the second forward primer being as follows: SEQ ID NO.3 As shown: AAGGCGCGTCTAGGTGGTTATGT, the nucleotide sequence of the second reverse primer is as follows: SEQ ID NO.4 As shown: TGTAGTTCCGCTTACCTCTCCAG; The nucleotide sequence of the universal primer is as follows: SEQ ID NO.5 Shown: ATTAGATACCCTGGGTAGTCC.

[0008] The third technical solution of the present invention: The kit is used in the preparation of products and / or systems for predicting the prognosis of laryngeal squamous cell carcinoma.

[0009] The fourth technical solution of the present invention: A system for predicting the prognosis of laryngeal squamous cell carcinoma based on microbial biomarkers, the system comprising a detection module, a data processing module, and a recurrence risk assessment module; The detection module detects the microbial markers of claim 1 in tissue samples of laryngeal squamous cell carcinoma based on quantitative PCR, and obtains the cycle thresholds of the microbial markers in the tissue samples. Ct value; The data processing module acquires the loop threshold. Ct Values ​​were obtained using universal primers as internal controls. -ΔCt Standardization of methods, based on microbial markers -ΔCt The size of the microorganism is assigned a score accordingly. The recurrence risk assessment module inputs the assigned score into the SF prognostic model for calculation, and assesses the patient's recurrence risk based on the calculation results.

[0010] Furthermore, the data processing module assigns scores to the corresponding microorganisms as follows: When Serratia- ΔCt < -8.3950, assign 1 point; when Fusobacterium spp. Fusobacterium of- ΔCtA score of 0 is assigned when the value is ≥-8.3950. When Fusobacterium serratia- ΔCt A score greater than -7.6800 is assigned 1 point; when Fusobacterium spp. Fusobacterium of- ΔCt If the value is ≤ -7.6800, a score of 0 is assigned.

[0011] Furthermore, the SF prognostic risk model is shown in the following equation (1): Prognostic risk score = Σ X i × β i ; in X i Assigning classifications to the fungal genus, β i As a coefficient, Serratia spp. Serratia The corresponding coefficient is 1.8221, for Fusobacterium. Fusobacterium The corresponding coefficient is 1.6051.

[0012] Furthermore, the recurrence risk assessment module evaluates the patient's recurrence risk based on the obtained prognostic risk score: when the prognostic risk score is greater than 2, the patient has a high risk of recurrence; when the prognostic risk score is less than 2, the patient has a low risk of recurrence.

[0013] Furthermore, the detection module includes detection reagents and / or instruments for detecting the microbial markers in tissue samples of laryngeal squamous cell carcinoma based on quantitative PCR.

[0014] Furthermore, the specific procedures for detecting the aforementioned microbial markers in tissue samples of laryngeal squamous cell carcinoma using quantitative PCR are as follows: Specific primers were designed for the 16S rRNA sequences of microbial biomarkers using NCBI's Primer-BLAST tool, and internal reference genes were introduced for data standardization.

[0015] The beneficial effects achieved by adopting this invention are as follows: This invention is the first to link recurrence of laryngeal squamous cell carcinoma with the microbial community, specifically the tumor-promoting microorganism genus *Fusobacterium*. Fusobacterium and the anti-cancer microorganism Serratia spp. Serratia This involves integrating data to detect the content of microorganisms using quantitative PCR, based on their cycle threshold. Ct The obtained -ΔCtAfter risk scoring, the risk score is calculated based on the SF prognostic risk model. Then, the risk of prognosis of laryngeal squamous cell carcinoma, especially the risk of recurrence, can be classified according to the calculation results. This can provide a new direction for the prediction of prognosis of laryngeal squamous cell carcinoma, especially the risk of recurrence within 5 years and the prediction of disease-free survival.

[0016] The prognostic model of this invention was compared with the prognostic prediction effects of systems based on single bacterial genus content and TNM staging. ROC curve analysis showed that the present invention had the highest accuracy in predicting the recurrence risk (disease-free survival) of laryngeal squamous cell carcinoma and the strongest generalization ability of the prognostic model. Attached Figure Description

[0017] Appendix Figure 1 This is an experimental overview diagram of an embodiment of the present invention.

[0018] Appendix Figure 2 Recurrence of laryngeal squamous cell carcinoma according to an embodiment of the present invention Serratia-Fusobacterium Establishment and validation of the prognostic scoring model (SF prognostic risk model); in: Figure 2 A is an analysis of the relationship between the NR group and the RC group in the LSCC cohort using the Mann-Whitney U test. Serratia and Fusobacterium The content difference; Figure 2 B represents the qPCR assay performed in the LSCC cohort. Serratia and Fusobacterium Correlation analysis between content and 16S rRNA sequencing results; Figure 2 Based on C Serratia and Fusobacterium The cutoff values ​​for the content were used to stratify LSCC cohort patients, and the log-rank test was used to assess disease-free survival (DFS). Figure 2 D represents the evaluation using Cox multivariate regression analysis in the LSCC cohort. Serratia and Fusobacterium The independent prognostic effect of content on DFS, with error bars representing 95% confidence intervals (CI). Figure 2 E is Serratia-Fusobacterium A schematic diagram of the calculation process for the prognostic scoring model (SF prognostic model).

[0019] Figure 2 F represents the value in the LSCC cohort based on... Serratia and FusobacteriumROC curve analysis was performed using the content, SF prognostic model and TNM staging system to evaluate the predictive ability of LSCC recurrence. Figure 2 G represents the risk stratification in the LSCC cohort based on the SF prognostic model for DFS analysis, and the log-rank test is used to assess the survival differences. Figure 2 H represents the number of centers in a multi-LSCC cohort, based on... Serratia and Fusobacterium ROC curve analysis was performed using the content, SF prognostic model, and TNM staging system to assess the predictive ability for LSCC recurrence. Figure 2 I represents the DFS analysis performed in the Multi-LSCC cohort based on the risk stratification of the SF prognostic model, and the log-rank test was used to assess the survival differences.

[0020] Appendix Figure 3 X-tile analysis was used to determine the method in this embodiment of the invention. Serratia (A) and Fusobacterium (B) The optimal cutoff value for the content. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to its embodiments; it should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0022] Unless otherwise specified, the experimental methods used in the following experiments are all conventional methods.

[0023] Unless otherwise specified, all materials and reagents used in the following experiments are commercially available.

[0024] The technical solution of this invention: Example 1: The following is a summary of the construction and validation of the microbial markers and their SF prognostic model of the present invention (see attached diagram for overview). Figure 1 ).

[0025] (I) Materials and Methods Samples are derived from groups This study included paraffin-embedded (FFPE) tissue samples from patients with eligible laryngeal squamous cell carcinoma (LSCC). All patients received laryngeal preservation therapy and were grouped according to postoperative recurrence.

[0026] All patients met the following inclusion criteria: ① Diagnosed with laryngeal squamous cell carcinoma (LSCC), confirmed by pathology, and meeting the AJCC 8th edition TNM staging criteria, receiving laryngeal-preserving treatment (including partial laryngectomy, radiotherapy or chemotherapy), with a postoperative follow-up period of no less than 5 years and complete clinical follow-up records, including recurrence status, disease-free survival (DFS), etc.

[0027] ②None of the patients used antibiotics or immunosuppressants before the operation to ensure that the microbiome was not disturbed by exogenous factors; ③ No history of other malignant tumors to avoid affecting the analysis of microbial characteristics. All patients and their families have signed informed consent forms and obtained approval from the ethics committee.

[0028] Exclusion criteria include: ① There was a significant bacterial or viral infection before or shortly after the operation; ② You have recently (within six months) received antibiotic, hormone, or immunosuppressive therapy; ③ Postoperative residual tumor or failure to complete standardized treatment plan; ④ Limited survival due to other systemic diseases or serious comorbidities; ⑤ Incomplete clinical data or pathological tissue samples; ⑥ The quality of sample DNA extraction does not meet the sequencing requirements, the sample is lost to follow-up or key data is missing during the follow-up process, and there are other factors that may affect the microbiome analysis, such as severe gastrointestinal diseases, autoimmune diseases or chronic inflammatory diseases.

[0029] Grouping criteria: 183 patients with laryngeal squamous cell carcinoma were included, and a total of 240 FFPE samples from laryngeal squamous cell carcinoma were collected. The specific groupings are as follows: ① Training cohort (LSCC cohort): 123 LSCC patients who underwent laryngeal preservation treatment at the Eye, Ear, Nose and Throat Hospital affiliated with Fudan University from 2015 to 2021 were included. A total of 180 FFPE tissue samples were collected and divided into non-recurrence group (NR group, 61 cases), recurrence group (RC group, 62 cases) and postoperative recurrence group (PRC group, 57 cases).

[0030] ② Multi-LSCC cohort: 60 LSCC patients from other hospitals between 2011 and 2020 were included, including 28 cases from the First Affiliated Hospital of Nanjing Medical University and 32 cases from Renji Hospital of Shanghai Jiao Tong University.

[0031] (1) Obtaining paraffin-embedded tissue samples and extracting DNA All FFPE tissue samples in this experiment were derived from postoperative pathological tissues and were fixed, dehydrated, embedded, and stored according to standard pathological procedures.

[0032] The specific process is as follows: ① Sample source: Postoperative pathological tissue was selected, and the tumor tissue area was confirmed by a pathologist to avoid interference from necrotic tissue or non-tumor tissue; ② Tissue fixation: Immediately after tissue collection, fix the tissue in 10% neutral formalin solution for 24-48 hours to ensure tissue structure integrity and prevent DNA degradation; ③ Dehydration and embedding: After dehydration with gradient alcohol and clearing with xylene, the tissue was embedded in paraffin and prepared according to the standard paraffin sectioning procedure; ④ Tissue sections: 5-10 μm thick continuous sections were cut from each sample and used for H&E staining to identify tissue regions and for DNA extraction.

[0033] This experiment used the GeneRead DNA FFPE kit (180134, Qiagen) to extract whole-genome DNA from LSCC tissue samples. The extraction procedure is as follows: ① Take 5 FFPE tissue sections with a thickness of 5-10 μm and place them in a 1.5 mL EP tube. Add 800 μL of dewaxing buffer, vortex to mix for 10 seconds, incubate at room temperature for 5 minutes, centrifuge at 12,000×g for 5 minutes, discard the supernatant, add 800 μL of 100% ethanol and mix well, centrifuge at 12,000×g for 5 minutes, discard the supernatant, repeat the ethanol washing once, and air dry the particles at room temperature to remove residual ethanol; ② Add 180 μL of lysis buffer and 20 μL of proteinase K, mix well, and incubate at 56°C for 1 hour to induce tissue lysis. Then incubate at 90°C for 1 hour to remove formaldehyde cross-linking and improve DNA extraction efficiency. Centrifuge at 12,000×g for 5 minutes and collect the lysis products. ③ Add 200 μL of binding buffer (Buffer AL) and 200 μL of 100% ethanol, mix well, transfer to a DNA extraction column, centrifuge at 12,000×g for 1 minute, and discard the eluent. Add 500 μL of washing buffer (Buffer AW1), centrifuge at 12,000×g for 1 minute, discard the eluent, then add 500 μL of washing buffer (Buffer AW2), centrifuge at 12,000×g for 3 minutes to remove impurities, proteins, and inhibitors. ④ Replace with a new 1.5 mL EP tube, add 30 μL of elution buffer (Buffer AE) to the center of the centrifuge column, let stand at room temperature for 5 minutes, centrifuge at 12,000×g for 2 minutes to collect the DNA solution, determine the DNA concentration using NanoDrop, ensure the A260 / A280 ratio is 1.8-2.0, and assess DNA integrity by agarose gel electrophoresis. Store qualified DNA at -80℃ to prevent degradation.

[0034] (2) Establishment and validation of a microbial prognostic model for recurrent laryngeal squamous cell carcinoma To accurately verify the presence of Serratia spp. in LSCC ( Serratia ) and Fusobacterium genus ( Fusobacterium The content of the two bacterial genera was determined by quantitative PCR (qPCR) using SYBR Green fluorescent dye provided by Jereh Biotechnology (China).

[0035] Specific primers targeting the 16S rRNA sequences of Serratia and Fusobacterium were designed using NCBI's Primer-BLAST tool (see Table 1), and internal reference genes (such as bacterial universal primers) were introduced. 16S rRNA Primers were used for data standardization (primer sequences are shown in Appendix 1) to avoid interference from non-target amplification. The cycle threshold of the sample ( Ct (value) through -ΔCt Standardize the method (using universal primers as internal references) to ensure data comparability.

[0036] Table 1 Primer Sequences

[0037] To assess the prognostic value of specific genus abundance, X-tile software (v.3.6.1) was used to calculate... Serratia and Fusobacterium Optimal classification threshold ( Figure 3 The results showed that... Serratia Low content ( -ΔCt Patients with a DFS <-8.3950) had significantly shorter DFS. Fusobacterium High content ( -ΔCt Patients with >-7.6800 also had significantly shorter DFS (P<0.0001). Figure 2 C). Further univariate and multivariate Cox regression analyses were used to assess the impact of potential confounding factors, and the results showed... Serratia and Fusobacterium The levels of all of these were independent risk factors for DFS (P = 0.0001). Figure 2 D). Based on this, the established Serratia-Fusobacterium The prognostic scoring model (SF prognostic risk model) is feasible for predicting the risk of LSCC recurrence. Figure 2 E).

[0038] The clinical cutoff value for bacterial content was dynamically determined using X-tile software (v3.6.1, USA): when the Serratia content in the sample was low ( -ΔCt When the concentration is ≤-8.3950, it indicates a weakened anti-cancer effect, and a risk score of 1 is assigned; while a higher concentration of Fusobacterium ( -ΔCtA value >-7.6800 indicates enhanced cancer-promoting activity, also assigned a risk score of 1. Based on multivariate Cox regression, a prognostic risk scoring formula (SF prognostic risk model) is constructed: Σ X i ×β i calculate( X i Assigning classifications to the fungal genus, β i (regression coefficients) Specifically, among them X i Assigning classifications to the fungal genus, β i As a coefficient, Serratia spp. Serratia Corresponding coefficients β i 1.8221, Fusobacterium spp. Fusobacterium Corresponding coefficients β i It is 1.6051. β i The results were obtained from the multivariate Cox regression model, and the specific results are shown in Table 2 below.

[0039] Table 2: variable regression coefficient β Standard value Wald p-value Serratia 1.8221 0.4701 15.0210 0.0001 Fusobacterium 1.6051 0.3174 25.5789 <0.0001 (3) Next, the nonparametric Mann-Whitney U test was used to assess the relative abundance and quantitative differences of significant genera among different groups, and the correlation between genera abundance and qPCR quantitative results was calculated by two-tailed Spearman correlation analysis.

[0040] To evaluate the sensitivity and specificity of specific bacterial genera in predicting LSCC recurrence, this study used receiver operating characteristic (ROC) curve analysis to calculate the area under the curve (AUC), determined the optimal cutoff value, and compared the prognostic predictive ability of the SF prognostic risk model, single bacterial genera content, and TNM staging system.

[0041] The Kaplan-Meier method was used to calculate disease-free survival (DFS) in patients with laryngeal squamous cell carcinoma, and the survival differences among different risk groups were compared using the log-rank test. Simultaneously, Cox proportional hazards regression models (univariate and multivariate analyses) were used to assess the independent effects of the SF prognostic risk model, clinicopathological features, and microbial abundance on DFS.

[0042] All statistical tests were two-tailed, and a p-value < 0.05 was considered statistically significant. All data analyses were performed using GraphPad Prism (v.10.0.3, USA) and IBM SPSS Statistics (v.20.0, USA).

[0043] The results are as follows Figure 2 As shown: In laryngeal squamous cell carcinoma tissue Serratia and Fusobacterium Content analysis: To detect LSCC Serratia and Fusobacterium The content was determined using quantitative real-time fluorescence PCR (qPCR) with specific bacterial primers. Serratia and Fusobacterium Quantitative analysis was performed on the content of [specific substance]. Experimental results showed that in the training cohort (LSCC cohort), the RC group [had a higher concentration of certain substances]. Serratia The content decreased significantly, while Fusobacterium The content was significantly increased (P<0.0001). Figure 2 A), and the qPCR detection results were consistent with the trend of genus abundance changes in 16S rRNA gene sequencing (P<0.0001, Figure 2 B).

[0044] (4) Recurrence of laryngeal squamous cell carcinoma Serratia-Fusobacterium Establishment and validation of the prognostic scoring model (SF prognostic model) The SF prognostic model was trained in the LSCC cohort (Eye, Ear, Nose and Throat Hospital Affiliated to Fudan University). Patients in the training cohort were divided into high-risk and low-risk groups based on the SF prognostic risk model (51 cases in the high-risk group and 72 cases in the low-risk group, corresponding to the recurrence risk grouping in the training cohort; i.e., the recurrence risk of patients in the high-risk group was 100%, and the recurrence risk of patients in the low-risk group was 0). Independent validation was then performed in a multi-center LSCC validation cohort (First Affiliated Hospital of Nanjing Medical University and Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine). ROC curve analysis was used to evaluate the accuracy of the SF prognostic risk model, single spore count, and the AJCC 8th edition TNM staging system in predicting LSCC recurrence. The results showed that the AUC value of the SF prognostic risk model was significantly higher than that of the single spore count and the TNM staging system (P<0.0001). Figure 2 F), and Kaplan-Meier survival analysis showed that the DFS in the high-risk group was significantly lower than that in the low-risk group (P<0.0001, Figure 2G). To further verify the generalization ability of the SF prognostic model, the model was externally validated in a Multi-LSCC cohort. The results showed that the predictive ability of the SF prognostic model (P = 0.0018) was consistently better than that of the TNM staging system (P = 0.4511). Figure 2 H), and the DFS analysis results were consistent (P = 0.0003, Figure 2I).

[0045] The above results fully demonstrate that the SF prognostic risk model can be used to predict the risk of LSCC recurrence and is superior to the traditional TNM staging system, thus having high clinical application value.

[0046] This invention is the first to link recurrence of laryngeal squamous cell carcinoma with the microbial community, based on microbial biomarkers ( Serratia and Fusobacterium A prognostic scoring model (SF prognostic risk model) was constructed, providing a new direction for the accurate prediction of postoperative recurrence of laryngeal squamous cell carcinoma. The model achieved AUC values ​​of 81.37% and 78.48% on the training set and multicenter validation set, respectively, significantly outperforming the traditional TNM staging system, providing a highly sensitive and specific risk assessment tool for clinical practice.

[0047] This invention is based on microbial biomarkers ( Serratia and Fusobacterium Using the SF prognostic model, the risk of relapse can be predicted, and people at risk of relapse can be identified early. This helps doctors develop targeted follow-up plans and interventions (such as probiotic or antibiotic treatment), thereby reducing the relapse rate, prolonging disease-free survival, and improving patients' quality of life.

[0048] This invention does not rely on complex next-generation sequencing technology. It only requires quantitative PCR to detect the content of specific bacterial groups. The operation is simple and inexpensive. By accurately stratifying patients, it can avoid overtreatment or undertreatment, reduce unnecessary consumption of medical resources, and lower the overall treatment cost.

[0049] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. The application of microbial biomarkers in the preparation of products and / or systems for predicting the prognosis of laryngeal squamous cell carcinoma, characterized in that, The microbial markers are from the genus Serratia. Serratia and Fusobacterium Fusobacterium composition.

2. A reagent kit, characterized in that, Its purpose is to detect the content of the microbial markers described in claim 1.

3. The reagent kit according to claim 2, characterized in that, It includes primers for detecting the microbial markers of claim 1, said primers comprising a first primer pair, a second primer pair, and a universal primer; The first primer pair includes a first forward primer and a first reverse primer, the nucleotide sequence of which is as follows: SEQ ID NO.1 As shown: GTTTCCCAGACATTACTCAC, the nucleotide sequence of the first reverse primer is as follows: SEQ ID NO.2 As shown: AGCTTTTAATTGAAGAGTTTG; The second primer pair includes a second forward primer and a second reverse primer, the nucleotide sequence of the second forward primer being as follows: SEQ ID NO.3 As shown: AAGGCGCGTCTAGGTGGTTATGT, the nucleotide sequence of the second reverse primer is as follows: SEQ ID NO.4 As shown: TGTAGTTCCGCTTACCTCTCCAG; The nucleotide sequence of the universal primer is as follows: SEQ ID NO.5 Shown: ATTAGATACCCTGGGTAGTCC.

4. The use of the kit according to claim 2 or 3 in the preparation of a system for predicting the prognosis of laryngeal squamous cell carcinoma.

5. A system based on microbial biomarkers for predicting the prognosis of laryngeal squamous cell carcinoma, characterized in that, The system includes a detection module, a data processing module, and a recurrence risk assessment module; The detection module detects the microbial markers of claim 1 in tissue samples of laryngeal squamous cell carcinoma based on quantitative PCR, and obtains the cycle thresholds of the microbial markers in the tissue samples. Ct value; The data processing module acquires the loop threshold. Ct Values ​​were obtained using universal primers as internal controls. -Δ Ct Standardization of methods, based on microbial markers -ΔCt The size of the microorganism is assigned a score accordingly. The recurrence risk assessment module inputs the assigned score into the SF prognostic risk model to calculate the prognostic risk score, and assesses the patient's recurrence risk based on the calculation results.

6. The system for predicting the prognosis of laryngeal squamous cell carcinoma based on microbial biomarkers according to claim 5, characterized in that, The data processing module assigns scores to the corresponding microorganisms as follows: When Serratia spp. -ΔCt When < -8.3950, assign 1 point, when it is Fusobacterium spp. Fusobacterium of - ΔCt A score of 0 is assigned when the value is ≥-8.3950. When Fusobacterium serratia -ΔCt When the score is > -7.6800, 1 point is awarded, and the score is in the Fusobacterium genus. Fusobacterium of- ΔCt If the value is ≤ -7.6800, a score of 0 is assigned.

7. The system for predicting the prognosis of laryngeal squamous cell carcinoma based on microbial biomarkers according to claim 5, characterized in that, The SF prognostic risk model is shown in the following formula (1): Prognostic risk score = Σ X i × β i ; in X i Assigning classifications to the fungal genus, β i As a coefficient, Serratia spp. Serratia The corresponding coefficient is 1.8221, for Fusobacterium. Fusobacterium The corresponding coefficient is 1.6051.

8. The system for predicting the prognosis of laryngeal squamous cell carcinoma based on microbial biomarkers according to claim 7, characterized in that, The recurrence risk assessment module evaluates the patient's recurrence risk based on the obtained prognostic risk score: when the prognostic risk score is greater than 2, the patient has a high risk of recurrence. When the prognostic risk score is less than 2, the patient has a low risk of recurrence.

9. The system for predicting the prognosis of laryngeal squamous cell carcinoma based on microbial biomarkers according to claim 5, characterized in that, The detection module includes detection reagents and / or instruments for detecting the microbial markers of claim 1 in tissue samples of laryngeal squamous cell carcinoma based on quantitative PCR.

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